Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations243787
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.1 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Categorical7
Boolean4
Text1

Alerts

AccountAge is highly overall correlated with TotalChargesHigh correlation
TotalCharges is highly overall correlated with AccountAgeHigh correlation
MonthlyCharges has unique values Unique
TotalCharges has unique values Unique
ViewingHoursPerWeek has unique values Unique
AverageViewingDuration has unique values Unique
UserRating has unique values Unique
CustomerID has unique values Unique
ContentDownloadsPerMonth has 4851 (2.0%) zeros Zeros
SupportTicketsPerMonth has 24292 (10.0%) zeros Zeros
WatchlistSize has 9654 (4.0%) zeros Zeros

Reproduction

Analysis started2025-08-03 14:26:23.859405
Analysis finished2025-08-03 14:26:33.968107
Duration10.11 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

AccountAge
Real number (ℝ)

High correlation 

Distinct119
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.083758
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.016439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q130
median60
Q390
95-th percentile114
Maximum119
Range118
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.285143
Coefficient of variation (CV)0.57062249
Kurtosis-1.1992817
Mean60.083758
Median Absolute Deviation (MAD)30
Skewness-0.002506029
Sum14647639
Variance1175.471
MonotonicityNot monotonic
2025-08-03T09:26:34.056144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 2168
 
0.9%
95 2157
 
0.9%
19 2148
 
0.9%
74 2143
 
0.9%
99 2141
 
0.9%
87 2131
 
0.9%
60 2131
 
0.9%
92 2130
 
0.9%
13 2114
 
0.9%
76 2114
 
0.9%
Other values (109) 222410
91.2%
ValueCountFrequency (%)
1 2015
0.8%
2 1969
0.8%
3 2013
0.8%
4 2028
0.8%
5 1967
0.8%
6 2088
0.9%
7 2054
0.8%
8 1974
0.8%
9 2001
0.8%
10 1953
0.8%
ValueCountFrequency (%)
119 2071
0.8%
118 2008
0.8%
117 2066
0.8%
116 2064
0.8%
115 2017
0.8%
114 2006
0.8%
113 1990
0.8%
112 2102
0.9%
111 2090
0.9%
110 2069
0.8%

MonthlyCharges
Real number (ℝ)

Unique 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.490695
Minimum4.9900615
Maximum19.989957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.092688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.9900615
5-th percentile5.7419177
Q18.7385432
median12.495555
Q316.23816
95-th percentile19.226437
Maximum19.989957
Range14.999895
Interquartile range (IQR)7.499617

Descriptive statistics

Standard deviation4.3276154
Coefficient of variation (CV)0.34646716
Kurtosis-1.2015094
Mean12.490695
Median Absolute Deviation (MAD)3.7495368
Skewness-0.0035843795
Sum3045068.9
Variance18.728255
MonotonicityNot monotonic
2025-08-03T09:26:34.208997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.0552151 1
 
< 0.1%
12.88378931 1
 
< 0.1%
14.83776403 1
 
< 0.1%
5.887315074 1
 
< 0.1%
13.36096719 1
 
< 0.1%
17.18843146 1
 
< 0.1%
19.33623845 1
 
< 0.1%
13.53752198 1
 
< 0.1%
19.15785687 1
 
< 0.1%
16.56393002 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
4.990061547 1
< 0.1%
4.990112126 1
< 0.1%
4.990126433 1
< 0.1%
4.990184837 1
< 0.1%
4.990326069 1
< 0.1%
4.990379425 1
< 0.1%
4.990441189 1
< 0.1%
4.99048508 1
< 0.1%
4.990630161 1
< 0.1%
4.99079638 1
< 0.1%
ValueCountFrequency (%)
19.98995687 1
< 0.1%
19.98982121 1
< 0.1%
19.98975519 1
< 0.1%
19.98974143 1
< 0.1%
19.98961734 1
< 0.1%
19.98951769 1
< 0.1%
19.98948893 1
< 0.1%
19.98942455 1
< 0.1%
19.98941153 1
< 0.1%
19.98936381 1
< 0.1%

TotalCharges
Real number (ℝ)

High correlation  Unique 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750.74102
Minimum4.9911544
Maximum2378.7238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.244816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.9911544
5-th percentile71.255304
Q1329.14703
median649.87849
Q31089.3174
95-th percentile1766.0831
Maximum2378.7238
Range2373.7327
Interquartile range (IQR)760.17034

Descriptive statistics

Standard deviation523.07327
Coefficient of variation (CV)0.69674263
Kurtosis-0.26204684
Mean750.74102
Median Absolute Deviation (MAD)364.48602
Skewness0.69406771
Sum1.830209 × 108
Variance273605.65
MonotonicityNot monotonic
2025-08-03T09:26:34.278430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221.104302 1
 
< 0.1%
1159.541038 1
 
< 0.1%
1721.180628 1
 
< 0.1%
359.1262195 1
 
< 0.1%
1042.155441 1
 
< 0.1%
1598.524126 1
 
< 0.1%
1411.545407 1
 
< 0.1%
974.7015825 1
 
< 0.1%
1264.418553 1
 
< 0.1%
265.0228803 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
4.991154354 1
< 0.1%
4.999861067 1
< 0.1%
5.018304003 1
< 0.1%
5.033848266 1
< 0.1%
5.047382883 1
< 0.1%
5.053362973 1
< 0.1%
5.065943606 1
< 0.1%
5.066787708 1
< 0.1%
5.081493533 1
< 0.1%
5.08541407 1
< 0.1%
ValueCountFrequency (%)
2378.723844 1
< 0.1%
2378.454499 1
< 0.1%
2377.774305 1
< 0.1%
2377.224228 1
< 0.1%
2375.495398 1
< 0.1%
2374.015612 1
< 0.1%
2373.837145 1
< 0.1%
2373.70029 1
< 0.1%
2373.557275 1
< 0.1%
2372.488799 1
< 0.1%

SubscriptionType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Standard
81920 
Basic
81050 
Premium
80817 

Length

Max length8
Median length7
Mean length6.6711063
Min length5

Characters and Unicode

Total characters1626329
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowBasic
3rd rowBasic
4th rowBasic
5th rowPremium

Common Values

ValueCountFrequency (%)
Standard 81920
33.6%
Basic 81050
33.2%
Premium 80817
33.2%

Length

2025-08-03T09:26:34.310225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:34.334461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
standard 81920
33.6%
basic 81050
33.2%
premium 80817
33.2%

Most occurring characters

ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1626329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1626329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1626329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 244890
15.1%
d 163840
10.1%
r 162737
10.0%
i 161867
10.0%
m 161634
9.9%
S 81920
 
5.0%
t 81920
 
5.0%
n 81920
 
5.0%
B 81050
 
5.0%
s 81050
 
5.0%
Other values (4) 323501
19.9%

PaymentMethod
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Electronic check
61313 
Credit card
60924 
Bank transfer
60797 
Mailed check
60753 

Length

Max length16
Median length13
Mean length13.005488
Min length11

Characters and Unicode

Total characters3170569
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMailed check
2nd rowCredit card
3rd rowMailed check
4th rowElectronic check
5th rowElectronic check

Common Values

ValueCountFrequency (%)
Electronic check 61313
25.2%
Credit card 60924
25.0%
Bank transfer 60797
24.9%
Mailed check 60753
24.9%

Length

2025-08-03T09:26:34.360735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:34.382006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
check 122066
25.0%
electronic 61313
12.6%
credit 60924
12.5%
card 60924
12.5%
bank 60797
12.5%
transfer 60797
12.5%
mailed 60753
12.5%

Most occurring characters

ValueCountFrequency (%)
c 427682
13.5%
e 365853
11.5%
r 304755
9.6%
243787
 
7.7%
a 243271
 
7.7%
t 183034
 
5.8%
i 182990
 
5.8%
n 182907
 
5.8%
k 182863
 
5.8%
d 182601
 
5.8%
Other values (9) 670826
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3170569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 427682
13.5%
e 365853
11.5%
r 304755
9.6%
243787
 
7.7%
a 243271
 
7.7%
t 183034
 
5.8%
i 182990
 
5.8%
n 182907
 
5.8%
k 182863
 
5.8%
d 182601
 
5.8%
Other values (9) 670826
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3170569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 427682
13.5%
e 365853
11.5%
r 304755
9.6%
243787
 
7.7%
a 243271
 
7.7%
t 183034
 
5.8%
i 182990
 
5.8%
n 182907
 
5.8%
k 182863
 
5.8%
d 182601
 
5.8%
Other values (9) 670826
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3170569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 427682
13.5%
e 365853
11.5%
r 304755
9.6%
243787
 
7.7%
a 243271
 
7.7%
t 183034
 
5.8%
i 182990
 
5.8%
n 182907
 
5.8%
k 182863
 
5.8%
d 182601
 
5.8%
Other values (9) 670826
21.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
False
121980 
True
121807 
ValueCountFrequency (%)
False 121980
50.0%
True 121807
50.0%
2025-08-03T09:26:34.400668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ContentType
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Both
81737 
TV Shows
81145 
Movies
80905 

Length

Max length8
Median length6
Mean length5.9951433
Min length4

Characters and Unicode

Total characters1461538
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBoth
2nd rowMovies
3rd rowMovies
4th rowTV Shows
5th rowTV Shows

Common Values

ValueCountFrequency (%)
Both 81737
33.5%
TV Shows 81145
33.3%
Movies 80905
33.2%

Length

2025-08-03T09:26:34.423644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:34.442856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
both 81737
25.2%
tv 81145
25.0%
shows 81145
25.0%
movies 80905
24.9%

Most occurring characters

ValueCountFrequency (%)
o 243787
16.7%
h 162882
11.1%
s 162050
11.1%
B 81737
 
5.6%
t 81737
 
5.6%
T 81145
 
5.6%
V 81145
 
5.6%
81145
 
5.6%
S 81145
 
5.6%
w 81145
 
5.6%
Other values (4) 323620
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 243787
16.7%
h 162882
11.1%
s 162050
11.1%
B 81737
 
5.6%
t 81737
 
5.6%
T 81145
 
5.6%
V 81145
 
5.6%
81145
 
5.6%
S 81145
 
5.6%
w 81145
 
5.6%
Other values (4) 323620
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 243787
16.7%
h 162882
11.1%
s 162050
11.1%
B 81737
 
5.6%
t 81737
 
5.6%
T 81145
 
5.6%
V 81145
 
5.6%
81145
 
5.6%
S 81145
 
5.6%
w 81145
 
5.6%
Other values (4) 323620
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 243787
16.7%
h 162882
11.1%
s 162050
11.1%
B 81737
 
5.6%
t 81737
 
5.6%
T 81145
 
5.6%
V 81145
 
5.6%
81145
 
5.6%
S 81145
 
5.6%
w 81145
 
5.6%
Other values (4) 323620
22.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
False
122035 
True
121752 
ValueCountFrequency (%)
False 122035
50.1%
True 121752
49.9%
2025-08-03T09:26:34.458670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

DeviceRegistered
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Computer
61147 
Tablet
61143 
Mobile
60914 
TV
60583 

Length

Max length8
Median length6
Mean length5.5076112
Min length2

Characters and Unicode

Total characters1342684
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowTablet
3rd rowComputer
4th rowTablet
5th rowTV

Common Values

ValueCountFrequency (%)
Computer 61147
25.1%
Tablet 61143
25.1%
Mobile 60914
25.0%
TV 60583
24.9%

Length

2025-08-03T09:26:34.482376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:34.503800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
computer 61147
25.1%
tablet 61143
25.1%
mobile 60914
25.0%
tv 60583
24.9%

Most occurring characters

ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1342684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1342684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1342684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 183204
13.6%
t 122290
9.1%
o 122061
9.1%
b 122057
9.1%
l 122057
9.1%
T 121726
 
9.1%
C 61147
 
4.6%
m 61147
 
4.6%
p 61147
 
4.6%
u 61147
 
4.6%
Other values (5) 304701
22.7%

ViewingHoursPerWeek
Real number (ℝ)

Unique 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.502179
Minimum1.0000654
Maximum39.999723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.534465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000654
5-th percentile2.9556786
Q110.763953
median20.523116
Q330.219396
95-th percentile38.02732
Maximum39.999723
Range38.999658
Interquartile range (IQR)19.455443

Descriptive statistics

Standard deviation11.243753
Coefficient of variation (CV)0.54841748
Kurtosis-1.1998167
Mean20.502179
Median Absolute Deviation (MAD)9.7293099
Skewness-0.0013398264
Sum4998164.7
Variance126.42199
MonotonicityNot monotonic
2025-08-03T09:26:34.570985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.75810391 1
 
< 0.1%
17.59053039 1
 
< 0.1%
35.68335488 1
 
< 0.1%
5.693638995 1
 
< 0.1%
19.23610809 1
 
< 0.1%
4.379284843 1
 
< 0.1%
31.6819025 1
 
< 0.1%
10.10222013 1
 
< 0.1%
14.87022052 1
 
< 0.1%
32.57026882 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
1.000065389 1
< 0.1%
1.000086097 1
< 0.1%
1.000133293 1
< 0.1%
1.00018081 1
< 0.1%
1.000248228 1
< 0.1%
1.000325918 1
< 0.1%
1.000397443 1
< 0.1%
1.000443967 1
< 0.1%
1.000756308 1
< 0.1%
1.000915633 1
< 0.1%
ValueCountFrequency (%)
39.99972314 1
< 0.1%
39.99971796 1
< 0.1%
39.99964292 1
< 0.1%
39.99961711 1
< 0.1%
39.99957754 1
< 0.1%
39.99916777 1
< 0.1%
39.9989975 1
< 0.1%
39.99883125 1
< 0.1%
39.9985596 1
< 0.1%
39.99834422 1
< 0.1%

AverageViewingDuration
Real number (ℝ)

Unique 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.264061
Minimum5.0005475
Maximum179.99928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.606559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.0005475
5-th percentile13.69635
Q148.382395
median92.249992
Q3135.90805
95-th percentile171.15799
Maximum179.99928
Range174.99873
Interquartile range (IQR)87.525653

Descriptive statistics

Standard deviation50.505243
Coefficient of variation (CV)0.54739887
Kurtosis-1.2008998
Mean92.264061
Median Absolute Deviation (MAD)43.760873
Skewness0.0027580626
Sum22492779
Variance2550.7796
MonotonicityNot monotonic
2025-08-03T09:26:34.641969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.53137733 1
 
< 0.1%
114.5204177 1
 
< 0.1%
98.66201871 1
 
< 0.1%
60.20356905 1
 
< 0.1%
123.5751547 1
 
< 0.1%
55.49882022 1
 
< 0.1%
116.293516 1
 
< 0.1%
87.49099203 1
 
< 0.1%
153.527901 1
 
< 0.1%
89.43429449 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
5.000547486 1
< 0.1%
5.000937119 1
< 0.1%
5.002643095 1
< 0.1%
5.002748913 1
< 0.1%
5.00341399 1
< 0.1%
5.003741659 1
< 0.1%
5.004575147 1
< 0.1%
5.004932694 1
< 0.1%
5.005317525 1
< 0.1%
5.005780925 1
< 0.1%
ValueCountFrequency (%)
179.9992751 1
< 0.1%
179.9990502 1
< 0.1%
179.9990248 1
< 0.1%
179.998513 1
< 0.1%
179.9984585 1
< 0.1%
179.9978052 1
< 0.1%
179.9976453 1
< 0.1%
179.9968864 1
< 0.1%
179.9966445 1
< 0.1%
179.9964961 1
< 0.1%

ContentDownloadsPerMonth
Real number (ℝ)

Zeros 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.503513
Minimum0
Maximum49
Zeros4851
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.676993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median24
Q337
95-th percentile47
Maximum49
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.421174
Coefficient of variation (CV)0.58853494
Kurtosis-1.2013526
Mean24.503513
Median Absolute Deviation (MAD)13
Skewness-0.00042730885
Sum5973638
Variance207.97025
MonotonicityNot monotonic
2025-08-03T09:26:34.712359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 4999
 
2.1%
8 4996
 
2.0%
39 4995
 
2.0%
23 4959
 
2.0%
40 4952
 
2.0%
43 4952
 
2.0%
44 4951
 
2.0%
4 4949
 
2.0%
33 4929
 
2.0%
42 4922
 
2.0%
Other values (40) 194183
79.7%
ValueCountFrequency (%)
0 4851
2.0%
1 4763
2.0%
2 4817
2.0%
3 4914
2.0%
4 4949
2.0%
5 4897
2.0%
6 4875
2.0%
7 4860
2.0%
8 4996
2.0%
9 4814
2.0%
ValueCountFrequency (%)
49 4877
2.0%
48 4838
2.0%
47 4776
2.0%
46 4867
2.0%
45 4795
2.0%
44 4951
2.0%
43 4952
2.0%
42 4922
2.0%
41 4812
2.0%
40 4952
2.0%

GenrePreference
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Comedy
49060 
Fantasy
48955 
Drama
48744 
Action
48690 
Sci-Fi
48338 

Length

Max length7
Median length6
Mean length6.0008655
Min length5

Characters and Unicode

Total characters1462933
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSci-Fi
2nd rowAction
3rd rowFantasy
4th rowDrama
5th rowComedy

Common Values

ValueCountFrequency (%)
Comedy 49060
20.1%
Fantasy 48955
20.1%
Drama 48744
20.0%
Action 48690
20.0%
Sci-Fi 48338
19.8%

Length

2025-08-03T09:26:34.746764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:34.770528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
comedy 49060
20.1%
fantasy 48955
20.1%
drama 48744
20.0%
action 48690
20.0%
sci-fi 48338
19.8%

Most occurring characters

ValueCountFrequency (%)
a 195398
13.4%
i 145366
 
9.9%
y 98015
 
6.7%
m 97804
 
6.7%
o 97750
 
6.7%
t 97645
 
6.7%
n 97645
 
6.7%
F 97293
 
6.7%
c 97028
 
6.6%
C 49060
 
3.4%
Other values (8) 389929
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 195398
13.4%
i 145366
 
9.9%
y 98015
 
6.7%
m 97804
 
6.7%
o 97750
 
6.7%
t 97645
 
6.7%
n 97645
 
6.7%
F 97293
 
6.7%
c 97028
 
6.6%
C 49060
 
3.4%
Other values (8) 389929
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 195398
13.4%
i 145366
 
9.9%
y 98015
 
6.7%
m 97804
 
6.7%
o 97750
 
6.7%
t 97645
 
6.7%
n 97645
 
6.7%
F 97293
 
6.7%
c 97028
 
6.6%
C 49060
 
3.4%
Other values (8) 389929
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 195398
13.4%
i 145366
 
9.9%
y 98015
 
6.7%
m 97804
 
6.7%
o 97750
 
6.7%
t 97645
 
6.7%
n 97645
 
6.7%
F 97293
 
6.7%
c 97028
 
6.6%
C 49060
 
3.4%
Other values (8) 389929
26.7%

UserRating
Real number (ℝ)

Unique 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0027127
Minimum1.0000074
Maximum4.9999894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.803480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000074
5-th percentile1.2026707
Q12.000853
median3.0022614
Q34.0021573
95-th percentile4.8019608
Maximum4.9999894
Range3.999982
Interquartile range (IQR)2.0013043

Descriptive statistics

Standard deviation1.1552591
Coefficient of variation (CV)0.38473848
Kurtosis-1.2018115
Mean3.0027127
Median Absolute Deviation (MAD)1.0006059
Skewness-0.00095780411
Sum732022.33
Variance1.3346237
MonotonicityNot monotonic
2025-08-03T09:26:34.840240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.176497515 1
 
< 0.1%
1.695150915 1
 
< 0.1%
2.295504648 1
 
< 0.1%
2.696378122 1
 
< 0.1%
4.882394783 1
 
< 0.1%
1.745561648 1
 
< 0.1%
2.723127467 1
 
< 0.1%
2.313576574 1
 
< 0.1%
3.045026176 1
 
< 0.1%
3.71845463 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
ValueCountFrequency (%)
1.000007378 1
< 0.1%
1.000039325 1
< 0.1%
1.000049907 1
< 0.1%
1.000052364 1
< 0.1%
1.000057666 1
< 0.1%
1.000068362 1
< 0.1%
1.000080538 1
< 0.1%
1.000082498 1
< 0.1%
1.000104797 1
< 0.1%
1.000130054 1
< 0.1%
ValueCountFrequency (%)
4.999989412 1
< 0.1%
4.999982428 1
< 0.1%
4.999973277 1
< 0.1%
4.99996787 1
< 0.1%
4.999942379 1
< 0.1%
4.999936482 1
< 0.1%
4.999934065 1
< 0.1%
4.999909322 1
< 0.1%
4.999849698 1
< 0.1%
4.999844547 1
< 0.1%

SupportTicketsPerMonth
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.504186
Minimum0
Maximum9
Zeros24292
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.868141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8725484
Coefficient of variation (CV)0.63775082
Kurtosis-1.2255379
Mean4.504186
Median Absolute Deviation (MAD)3
Skewness-0.00089641856
Sum1098062
Variance8.251534
MonotonicityNot monotonic
2025-08-03T09:26:34.889906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 24626
10.1%
4 24618
10.1%
2 24477
10.0%
9 24435
10.0%
8 24400
10.0%
3 24360
10.0%
6 24296
10.0%
0 24292
10.0%
1 24283
10.0%
5 24000
9.8%
ValueCountFrequency (%)
0 24292
10.0%
1 24283
10.0%
2 24477
10.0%
3 24360
10.0%
4 24618
10.1%
5 24000
9.8%
6 24296
10.0%
7 24626
10.1%
8 24400
10.0%
9 24435
10.0%
ValueCountFrequency (%)
9 24435
10.0%
8 24400
10.0%
7 24626
10.1%
6 24296
10.0%
5 24000
9.8%
4 24618
10.1%
3 24360
10.0%
2 24477
10.0%
1 24283
10.0%
0 24292
10.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Female
121930 
Male
121857 

Length

Max length6
Median length6
Mean length5.0002994
Min length4

Characters and Unicode

Total characters1219008
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 121930
50.0%
Male 121857
50.0%

Length

2025-08-03T09:26:34.917722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:34.936796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 121930
50.0%
male 121857
50.0%

Most occurring characters

ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1219008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1219008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1219008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 365717
30.0%
a 243787
20.0%
l 243787
20.0%
F 121930
 
10.0%
m 121930
 
10.0%
M 121857
 
10.0%

WatchlistSize
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.018508
Minimum0
Maximum24
Zeros9654
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:34.957495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile23
Maximum24
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.1930342
Coefficient of variation (CV)0.59849644
Kurtosis-1.1995119
Mean12.018508
Median Absolute Deviation (MAD)6
Skewness-0.0045001718
Sum2929956
Variance51.739741
MonotonicityNot monotonic
2025-08-03T09:26:34.984868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
16 9945
 
4.1%
14 9882
 
4.1%
10 9862
 
4.0%
17 9860
 
4.0%
19 9839
 
4.0%
7 9825
 
4.0%
12 9820
 
4.0%
21 9819
 
4.0%
18 9799
 
4.0%
11 9793
 
4.0%
Other values (15) 145343
59.6%
ValueCountFrequency (%)
0 9654
4.0%
1 9600
3.9%
2 9691
4.0%
3 9652
4.0%
4 9698
4.0%
5 9705
4.0%
6 9769
4.0%
7 9825
4.0%
8 9745
4.0%
9 9739
4.0%
ValueCountFrequency (%)
24 9618
3.9%
23 9684
4.0%
22 9737
4.0%
21 9819
4.0%
20 9707
4.0%
19 9839
4.0%
18 9799
4.0%
17 9860
4.0%
16 9945
4.1%
15 9759
4.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
True
122085 
False
121702 
ValueCountFrequency (%)
True 122085
50.1%
False 121702
49.9%
2025-08-03T09:26:35.005468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
True
122180 
False
121607 
ValueCountFrequency (%)
True 122180
50.1%
False 121607
49.9%
2025-08-03T09:26:35.019906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CustomerID
Text

Unique 

Distinct243787
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-08-03T09:26:35.170766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2437870
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243787 ?
Unique (%)100.0%

Sample

1st rowCB6SXPNVZA
2nd rowS7R2G87O09
3rd rowEASDC20BDT
4th rowNPF69NT69N
5th row4LGYPK7VOL
ValueCountFrequency (%)
cb6sxpnvza 1
 
< 0.1%
qfp5alfkj5 1
 
< 0.1%
a8421ll8kc 1
 
< 0.1%
iqnesr4w65 1
 
< 0.1%
easdc20bdt 1
 
< 0.1%
npf69nt69n 1
 
< 0.1%
4lgypk7vol 1
 
< 0.1%
jy5hs0gwhw 1
 
< 0.1%
79xso6p5o3 1
 
< 0.1%
2ldc9aq3c5 1
 
< 0.1%
Other values (243777) 243777
> 99.9%
2025-08-03T09:26:35.344839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 68214
 
2.8%
6 68024
 
2.8%
X 68013
 
2.8%
G 67974
 
2.8%
O 67962
 
2.8%
N 67961
 
2.8%
Z 67949
 
2.8%
R 67948
 
2.8%
D 67937
 
2.8%
1 67936
 
2.8%
Other values (26) 1757952
72.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2437870
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 68214
 
2.8%
6 68024
 
2.8%
X 68013
 
2.8%
G 67974
 
2.8%
O 67962
 
2.8%
N 67961
 
2.8%
Z 67949
 
2.8%
R 67948
 
2.8%
D 67937
 
2.8%
1 67936
 
2.8%
Other values (26) 1757952
72.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2437870
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 68214
 
2.8%
6 68024
 
2.8%
X 68013
 
2.8%
G 67974
 
2.8%
O 67962
 
2.8%
N 67961
 
2.8%
Z 67949
 
2.8%
R 67948
 
2.8%
D 67937
 
2.8%
1 67936
 
2.8%
Other values (26) 1757952
72.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2437870
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 68214
 
2.8%
6 68024
 
2.8%
X 68013
 
2.8%
G 67974
 
2.8%
O 67962
 
2.8%
N 67961
 
2.8%
Z 67949
 
2.8%
R 67948
 
2.8%
D 67937
 
2.8%
1 67936
 
2.8%
Other values (26) 1757952
72.1%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
199605 
1
44182 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243787
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%

Length

2025-08-03T09:26:35.374892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T09:26:35.391363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 199605
81.9%
1 44182
 
18.1%

Interactions

2025-08-03T09:26:33.041691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.218036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.564663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.947770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.283114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.620420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.954159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.362158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.708481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.078413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.257997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.600419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.981802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.320397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.657625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.989241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.399190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.743553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.113306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.293529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.636172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.017158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.358463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.695460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.041707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.437085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.778793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.149093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.330291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.671677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.050375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.394705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.731669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.080557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.474038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.812371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.185821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.382880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.709773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.104159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.432502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.769713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.116999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.513218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.847389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.221989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.420543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.747109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.143486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.471939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.806998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.153872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.552016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.883276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.256723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.457027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.783488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.179353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.509979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.843871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.255079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.590669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.932671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.293397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.494763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.821350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.215324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.548655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.882658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.292308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.633347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.974609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.327059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.528752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:30.856139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.249527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.584338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:31.917737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.327407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:32.670420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T09:26:33.008136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-03T09:26:35.415180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AccountAgeAverageViewingDurationChurnContentDownloadsPerMonthContentTypeDeviceRegisteredGenderGenrePreferenceMonthlyChargesMultiDeviceAccessPaperlessBillingParentalControlPaymentMethodSubscriptionTypeSubtitlesEnabledSupportTicketsPerMonthTotalChargesUserRatingViewingHoursPerWeekWatchlistSize
AccountAge1.0000.0000.1980.0010.0020.0020.0000.0010.0020.0050.0000.0000.0010.0020.000-0.0020.8560.000-0.002-0.003
AverageViewingDuration0.0001.0000.147-0.0020.0000.0030.0000.000-0.0010.0030.0000.0000.0000.0000.000-0.000-0.001-0.0000.0010.001
Churn0.1980.1471.0000.1290.0130.0000.0070.0260.1000.0000.0000.0050.0310.0360.0120.0840.1350.0210.1280.021
ContentDownloadsPerMonth0.001-0.0020.1291.0000.0020.0000.0030.003-0.0000.0030.0000.0030.0020.0050.000-0.0000.0000.0010.0020.002
ContentType0.0020.0000.0130.0021.0000.0000.0040.0000.0040.0000.0000.0000.0030.0000.0000.0050.0000.0040.0000.000
DeviceRegistered0.0020.0030.0000.0000.0001.0000.0030.0000.0000.0030.0040.0000.0050.0000.0000.0000.0000.0030.0030.002
Gender0.0000.0000.0070.0030.0040.0031.0000.0000.0000.0000.0030.0000.0020.0000.0000.0000.0000.0020.0040.005
GenrePreference0.0010.0000.0260.0030.0000.0000.0001.0000.0020.0000.0000.0000.0000.0030.0020.0000.0000.0000.0000.001
MonthlyCharges0.002-0.0010.100-0.0000.0040.0000.0000.0021.0000.0000.0040.0050.0020.0000.0000.0000.4590.000-0.003-0.001
MultiDeviceAccess0.0050.0030.0000.0030.0000.0030.0000.0000.0001.0000.0000.0000.0000.0000.0010.0030.0080.0050.0000.000
PaperlessBilling0.0000.0000.0000.0000.0000.0040.0030.0000.0040.0001.0000.0000.0000.0020.0020.0050.0000.0000.0000.005
ParentalControl0.0000.0000.0050.0030.0000.0000.0000.0000.0050.0000.0001.0000.0000.0000.0020.0000.0010.0000.0000.004
PaymentMethod0.0010.0000.0310.0020.0030.0050.0020.0000.0020.0000.0000.0001.0000.0020.0020.0020.0000.0020.0000.000
SubscriptionType0.0020.0000.0360.0050.0000.0000.0000.0030.0000.0000.0020.0000.0021.0000.0000.0000.0000.0000.0000.004
SubtitlesEnabled0.0000.0000.0120.0000.0000.0000.0000.0020.0000.0010.0020.0020.0020.0001.0000.0030.0000.0000.0000.000
SupportTicketsPerMonth-0.002-0.0000.084-0.0000.0050.0000.0000.0000.0000.0030.0050.0000.0020.0000.0031.000-0.002-0.0000.0010.001
TotalCharges0.856-0.0010.1350.0000.0000.0000.0000.0000.4590.0080.0000.0010.0000.0000.000-0.0021.0000.000-0.003-0.002
UserRating0.000-0.0000.0210.0010.0040.0030.0020.0000.0000.0050.0000.0000.0020.0000.000-0.0000.0001.000-0.0030.003
ViewingHoursPerWeek-0.0020.0010.1280.0020.0000.0030.0040.000-0.0030.0000.0000.0000.0000.0000.0000.001-0.003-0.0031.000-0.001
WatchlistSize-0.0030.0010.0210.0020.0000.0020.0050.001-0.0010.0000.0050.0040.0000.0040.0000.001-0.0020.003-0.0011.000

Missing values

2025-08-03T09:26:33.415932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-03T09:26:33.672945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AccountAgeMonthlyChargesTotalChargesSubscriptionTypePaymentMethodPaperlessBillingContentTypeMultiDeviceAccessDeviceRegisteredViewingHoursPerWeekAverageViewingDurationContentDownloadsPerMonthGenrePreferenceUserRatingSupportTicketsPerMonthGenderWatchlistSizeParentalControlSubtitlesEnabledCustomerIDChurn
02011.055215221.104302PremiumMailed checkNoBothNoMobile36.75810463.53137710Sci-Fi2.1764984Male3NoNoCB6SXPNVZA0
1575.175208294.986882BasicCredit cardYesMoviesNoTablet32.45056825.72559518Action3.4786328Male23NoYesS7R2G87O090
27312.106657883.785952BasicMailed checkYesMoviesNoComputer7.39516057.36406123Fantasy4.2388246Male1YesYesEASDC20BDT0
3327.263743232.439774BasicElectronic checkNoTV ShowsNoTablet27.960389131.53750730Drama4.2760132Male24YesYesNPF69NT69N0
45716.953078966.325422PremiumElectronic checkYesTV ShowsNoTV20.08339745.35665320Comedy3.6161704Female0NoNo4LGYPK7VOL0
51137.295744824.419081PremiumMailed checkYesBothNoMobile21.67829097.09574635Comedy3.7211348Female2YesYesJY5HS0GWHW0
63812.340675468.945639PremiumBank transferNoBothNoComputer36.51276181.78299328Action4.0908689Female20NoYes79XSO6P5O30
7257.247550181.188753StandardElectronic checkYesTV ShowsNoTV16.355816154.52168210Fantasy3.4102212Female22NoNo2LDC9AQ3C50
82619.803233514.884050StandardBank transferNoMoviesNoTablet8.20292994.37521128Fantasy2.6799860Male5YesYes74DURHL3Y81
91418.842934263.801080StandardBank transferNoMoviesNoComputer38.560694122.0128900Comedy2.9934410Male18NoNoCY8S2R3A1T0
AccountAgeMonthlyChargesTotalChargesSubscriptionTypePaymentMethodPaperlessBillingContentTypeMultiDeviceAccessDeviceRegisteredViewingHoursPerWeekAverageViewingDurationContentDownloadsPerMonthGenrePreferenceUserRatingSupportTicketsPerMonthGenderWatchlistSizeParentalControlSubtitlesEnabledCustomerIDChurn
243777456.582492296.212157StandardCredit cardYesTV ShowsNoTV23.08793137.83232927Drama3.7480991Female12YesYesFQ2HIE4Z9G1
2437784611.598542533.532929BasicMailed checkYesTV ShowsNoComputer32.676961160.03174929Sci-Fi1.8013273Female3YesYesJNHOX08RU40
2437799415.2763031435.972490StandardElectronic checkYesMoviesNoComputer28.67772086.02592019Sci-Fi1.0501238Male24YesNoAWR6P119AJ0
2437801810.444138187.994487StandardMailed checkYesMoviesYesTV8.91434624.65808123Fantasy1.4179457Male3YesYesPQQRAZXQ5U0
2437813513.499269472.474410StandardElectronic checkNoTV ShowsYesComputer34.26211133.14782017Comedy3.5656723Male6NoNoPBWH0TU5H70
243782779.639902742.272460BasicMailed checkNoMoviesNoComputer13.50272980.36731247Sci-Fi3.6974511Male8YesNoFBZ38J108Z0
24378311713.0492571526.763053PremiumCredit cardNoTV ShowsYesTV24.96329159.81844135Comedy1.4497424Male20NoNoW4AO1Y6NAI0
24378411314.5145691640.146267PremiumCredit cardYesTV ShowsNoTV10.628728176.18609544Action4.0122176Male13YesYes0H3SWWI7IU0
243785718.140555126.983887PremiumBank transferYesTV ShowsNoTV30.466782153.38631536Fantasy2.1357897Female5NoYes63SJ44RT4A0
2437869011.5937741043.439704PremiumMailed checkNoBothNoTV24.97253784.82449811Action1.4288963Female1YesNoA6IN701VRY0